NDLI: ECG Beat Classification Using Mirrored Gauss Model

Content Provider	IEEE Xplore Digital Library
Author	Qunyi Zhou Xing Liu Huilong Duan
Copyright Year	2005
Description	Author affiliation: Dept. of Inf. Technol. & Electron. Eng., Zhejiang Univ. of Sci. & Technol., Hangzhou (Qunyi Zhou)
Abstract	Accurate electrocardiogram (ECG) beat classification is essential for automated detection of arrhythmias. A novel classification algorithm of the ECG beats, applying mirrored Gauss model (MGM) had been proposed in this paper. The MGM has strong morphological representation ability for QRS complex waves using curve fitting. With the MGM, the width of QRS complex wave could be extracted and applied to ECG beat classification easily, effectively and automatically. It was proved by experiment carrying out using all of ECG records in MIT-BIH Arrhythmia Database that the MGM is a promising algorithm for ECG beat classification. The whole classification accuracy is 93.93% for normal beats and 93.94% for premature ventricular contraction (PVC) beats
Starting Page	5587
Ending Page	5590
File Size	317522
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615752
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Gaussian processes Biomedical engineering Curve fitting Polynomials Morphology Information technology Educational technology Hospitals Educational institutions curving fitting Arrhythmias beat classification Gauss function
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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